Social Network Forensics: Tapping the Data Pool of Social Networks

 
Social Network Forensics: Tapping the Data Pool of Social Networks
Social Network Forensics:
Tapping the Data Pool of Social Networks

 Martin Mulazzani, Markus Huber and Edgar Weippl

                                  Abstract

    With hundreds of millions social network users worldwide, forensic data
 extraction on social networks has become an important research problem.
 The forensic data collection is however tightly connected to the service op-
 erator, which leads to data completeness and presentation format issues.
 Online social networks imply that all user communication is stored entirely
 at the service operator, without direct access for investigators. In this paper
 we identify important data sources and analytical methods for automated
 forensic analysis on social network user data. We furthermore show how
 these data sources can be evaluated in an automated fashion, and without
 the need of collaboration from the social network operator. While our pro-
 posed methods apply to the great majority of social networks, we show the
 feasibility of our approach on basis of Facebook.

    Keywords: Online forensics, social networks, visualization
1     Introduction

With the increasing usage of social networks and the emerge of cloud com-
puting, digital forensics faces novel research problems and challenges. The
number of users of these services increases steadily, with e.g. Facebook cur-
rently claiming to have 800 million users [4]. While traditional forensics relies
on the physical acquisition of hardware [13, 14] and the usage of hashsums to
ensure evidence reliability, this approach does not scale to cloud services and
their use of distributed datacenters. With the lack of standardized forensics
APIs as well as unified processes for service operators, isolated solutions are
still in widespread use. Another important aspect of forensics is the proper
visualization of data [22, 16] due to the vast amount of available data.

    It is furthermore hard to visualize gathered social networking data in
a way that can answer common questions of interest on a first sight, so
that people without technical background can understand it. This has been
shown for example in the case of the consolidated.db from Apple’s iPhone:
the file contained geolocation information which has been already outlined
in 2010 [21]. However, the consolidated.db problem got widespread attention
with the release of the iPhoneTracker software [6] in April 2011, which visu-
alized the collected data. Due to the iPhoneTracker software, Apple finally
had to review and change their data collection process[1].
In this paper we identify data sources of interest for forensic examinations
on social networks, and how they can be leveraged in an automated fashion.
We furthermore identify graphs of interest that can be generated from these
data sources and can answer many possible questions of a forensic examiner
on first sight. To the best of our knowledge there has not yet been any work
on forensic analysis of data from social networks without the collaboration of
the social network operator. We show example graphs and present possible
visualizations, which can and should be used for social network analysis and
will be released under an open source license.

    The rest of the paper is organized as follows: Section 2 gives an overview
of social networks, and how they are already used for conducting and solving
crimes. Section 3 shows what data can be extracted from social networks for
social network forensic investigation, while Section 4 explains the data and
how it can be visualized. We show the feasibility of our approach in Section 5
and conclude in Section 6.

2     Background

Social network forensics has to rely on a limited set of data sources in many
cases. Acquiring the server’s hard drives is not feasible, and leveraging the
service operator’s data directly requires the service operator’s cooperation.
If at all, the investigator can submit requests to the operator and may or
may not receive all the relevant data (e.g. written in the Facebook law en-
forcement guidelines published by the EFF [3]). This is in clear contradiction
to the guidelines for evidence collection [13], as the investigator is unable to
show that the evidence is authentic, complete, and reliable. Network forensic
frameworks like PyFlag [15] and Xplico [8] simply cannot see or access all
the data, as they are solely passive. Our approach, on the other hand, does
not require the cooperation of the social network operator, and can ensure
these properties due to the open nature of our collection methodology and
tools.

2.1      Data Acquisition

Before being able to analyze social network data the data has to be gathered
and acquired. While traditional forensic methods can be used to extract
artifacts from local webbrowser cache [2], numerous other ways are possi-
ble on the communication layer. These range from passive sniffing on the
network to active attacks like sniffing on unencrypted Wifis [20] or in com-
bination with ARP spoofing on LANs. The recently proposed friend in the
middle attack [19], which uses a third party extension for the social network
in combination with a traditional crawler component, could be used as well.
Crawling however is limited, as metadata and accurate timestamps are not
shown on webpages. They are only available by using the social network
APIs, which extend the available data of the webinterface. Even though it
would be possible to use passive logging on the communication layer, for
example in cases where a judge ordered lawful interception on the Internet
connection of a suspect, this approach is limited as well as it would take a
tremendous amount of time for collecting information, and completeness is
hardly possible. Furthermore, many social networks offer the possibility to
encrypt data on the communication layer by using HTTPS, rendering passive
attacks useless.

    While Facebook announced recently that users are now able to download
all their profile data, the data provided by our method is far superior com-
pared to the Facebook profile download option which lacks e.g., important
metadata and is thus not useful for forensics. In general, it is not possible for
a user to download everything that is connected to his or her profile on the
social network. Another interesting feature recently announced is Facebook
Timeline, which encourages users to never delete anything from the social
network, and to use it as an historic archive. This surely is interesting for
forensic examinations, as the user is less likely to delete data.

3     Social Network Data Pool

While social networks vary in features and architecture, we identify the fol-
lowing generic data sources to be of interest in forensic examinations on social
networks:

    • The social footprint: What is the social graph of the user, with whom
is he or she connected (“friend”)?

   • Communications pattern: How is the network used for communicating,
     what method is used, and with whom is the user communicating?

   • Pictures and videos: What pictures and videos were uploaded by the
     user, on which other peoples pictures is he or she tagged?

   • Times of activity: When is a specific user connected to the social net-
     work, when exactly did a specific activity of interest took place?

   • Apps: What apps is the user using, what is their purpose, and what
     information can be inferred in the social context?

   All this information cannot be found on a suspect’s hard drive, as it is
solely stored at the social network operator. Especially for people that use
the social network on a daily base a plethora of information is stored at the
social network operator. Facebook claims that more than 50% of its users
use it any given day, which would be something around 400 million users [4].
Sometimes information is cached locally, but this is not a reliable source of
information as it is neither complete nor stored persistently. Depending on
the implementation of the social network, the availability of data itself and
the possibility to retrieve the data via API calls can vary among different
social networks. However, most of this data can be extracted either directly,
or inferred with low overhead without the collaboration of the social network
operator. Once the data is available to the investigator, the full spectrum of
social network analysis can be conducted [23]. The easiest way for obtaining
the data is of course with the consent of the user, who can provide username
and password. While the data can be easily analyzed manually afterwards to
answer specific questions, the humongous amount of data requires automated
tools for an forensic examiner to see the full picture.

4     Visualizations

Based on the social network data pool we define the following graphs of
interest and visualizations for social network forensics.

4.1    Basic Visualizations

Social Interconnection Graph: It is trivial to retrieve the list of friends
from social networks. In most social networks this is public information, or
can be easily collected [12] even without entering the social circle of the ac-
count under investigation. However, it is not trivial to cluster these friends,
namely to find out who is connected with whom: is a specific contact part
of the cluster of work-friends, or are they directly related?
In our approach we use a feature of the Facebook API which allows an ap-
plication to query if two users are connected. This allows our software to
cluster the friends of a user into different groups e.g., people from work,
school, family and more, as the members of the groups are much more likely
to know each other. The graph can be represented as an undirected graph
G =< V, E > where V = v1 , v2 , ...vn is the set of friends of a user, and
E = (vx , vy ), ... is the set of edges that connects two nodes in case they are
friends on the social network. Highly connected nodes have a high degree,
representing well connected friends that know most of the suspect’s friends
as well. An example graph will be shown in Section 5.

   Social Interaction Graph: For many investigations it is of importance
to find out who communicated with whom. Various ways of communication
are possible among users, like wall posts, direct messages, group communica-
tion or following public announcements. Communication can be represented
as a directed graph G =< V, E > where the nodes V = v1 , v2 , ...vn are all the
friends while the edges E = (vx , vy ), ... are directed and the weight of (vx , vy )
is incremented for every message sent from vx to vy . In this paper, however,
we do not distinct between the different forms: direct messages, wall posts,
and tags are treated equally as these are the most direct form of communi-
cation. We leave finding different metrics for future work which would allow
the investigator to add custom weights to specific communication forms. An
investigator can easily identify the top communication partners on a first
sight, and compare them with e.g., obtained phone records. An example
graph generated by our tools will be shown in Section 5 as well.

   Complete Timeline: With social network users being online 24/7 by
using mobile clients on smartphones, the timeline becomes of increasing im-
portance. Not only activity of the user itself can be extracted, but also the
activity of all his or her friends. Often the times of activity can be seen
easily, if properly visualized. To make analysis feasible it is necessary to
use respectively allow different data layers: activity of the user, the friends,
group activities, reactions on events from friends, and so forth. It is also
crucial that the timeline is zoomable, to visualize time ranges of importance
- a single day can have easily more than 500 events for a given profile and
all his or her friends.

   Location Visualization: Geotagging and location applications are novel
features with increasing usage that needs to be reflected in forensic exami-
nations. With foursquare [5] and Facebook Places, just to name a few, the
geolocation information stored in social networks is growing steadily. While
our toolset is not yet available to extract geodata, we believe that this will
become more and more of an issue. Digital cameras as well as smartphones
automatically geotag pictures taken with the exact location. Up till now,
most social networks remove metadata during the transformation for picture
storage [10], but this might change in the future.

4.2    Advanced Visualizations & Information Inference

While the features discussed so far are rather straightforward, we believe
that the following list of advanced features and components should become
standard tools in forensics:

   Event tracking: For viral scammers and other malicious applications
that use the social network for propagation it might be of interest who or
what started such a series of events. Tracking such events is not straightfor-
ward, but with a collection of social network footprints of various users these
events can be easily dissected. This would allow insight into dissemination
characteristics and propagation tactics of scammers, as well as advanced an-
alytical capabilities.

   Timeline matching: In highly centralized systems such as online social
networks, an investigator has the benefit of consistent timestamps as they
are provided by the social network. The operators often run their won NTP
infrastructure, and keep the clocks consistent across thousands of servers.
This can then be used to match timelines of different profiles, and eventu-
ally create an exact timeline for a complete cluster of friends or even bigger.
While this has been proposed recently for the NTFS file system [17], we be-
lieve that this will be of importance for social networks and cloud computing
as well.

   Differential Snapshots: Once a forensic image of a user profile is col-
lected, at a later point in time the image might look completely different.
Therefore, the forensic framework must provide the functionality to not only
visualize the social network data of a user, but also the functionality to vi-
sualize differences with previous images of the same user.

5       Results

We implemented the data collection methodology outlined in [18] to collect
data from Facebook, currently the biggest social network service. We then
parsed the output and generated the graphs as seen below. Data acquisi-
tion takes approximately 20 minutes per account, which is in our opinion a
reasonable amount of time for data collection. The data currently includes
all the social connections, direct communication, pictures and much, much
more.

5.1     Results Visualization

For the social interconnection graph we used a feature from the Facebook
API that allows an application to query if two specific users are connected.
We iteratively tested if the first friend is in connection with the n − 1 friends
of the tested profile, then tested for the second the remaining n − 2, and so
forth. We then extracted the social interconnection graph and plotted it with
Gephi [9], an open source graph visualization tool, using the Fruchterman-
Reingold algorithm [11]. The nodes that seem to be from the same cluster
are colored accordingly without manual intervention, which makes cluster
analysis for a forensic examiner very convenient. An example of a social in-
terconnection graph from one of the authors can be seen in Figure 1. Please
note that the names of the profiles have been replaced by a random subset
of the list of computer scientists on Wikipedia [7]. As the replaced set is
random, it obviously does not show real connetions between the computer
scientists.

   With the data from Facebook it becomes possible to create different so-
cial interaction graphs. In our implementation we created different graphs
for different forms of interaction, while they could be easily integrated to a
complete social interaction graph. An example for a social interaction graph
based on tags in pictures on Facebook can be seen in Figure 2. The graph is
created using the following steps: (1) starting from an account under inves-
tigation, all pictures from all friends are collected, and searched for tagged
people. (2) People that are tagged in pictures, and not in the list of friends,
are ignored. (3) If the tagged person is in the friend list as well, an edge
is added between the two nodes pointing from the profile that uploaded the
picture to the profile that is tagged, or the weight increases by one if the
edge already exists. The edges are directed and weightened. An investigator
can find with this graph persons that have a tight social connection.

   Another form of social interaction graph can be created using direct mes-
sages: instead of using picture tags, an edge is added between two nodes
if the profile under investigation exchanged messages with the other profile.
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                                                                        Figure 1: Anonymized Social Interconnection Graph
Yuri Matiyasevich
                                                                                                           Marvin Minsky

                                               Thomas Sterling

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                             Kurt Gödel
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                            Shmuel Winograd
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                                                                                                                            Yukihiro Matsumoto
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                                          Alonzo Church                                                                                                Ken Kennedy

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                       Dennis E. Wisnosky

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                                                                                      Erik Demaine
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      Figure 2: Anonymized Social Interaction Graph using Picture Tags

Intuitively, an edge pointing to a node represents a message sent to that pro-
file. Again the edges are weightened, for the number of messages sent. An
example for a social interaction graph using direct message communication
can be seen in Figure 3.
   Our data collection method also allows automated creation of timelines.
While this is currently under development, an example for a timeline of events
on Facebook over a 24-hour period can be seen in Figure 4.

5.2     Threats to Validity

Whilst our method is novel and can be easily used in addition to already ex-
isting and deployed social network analysis methods (i.e., subpoena requests
Figure 3: Social interaction graph using direct messages

John Doe                                   7:12:50 AM
                                                                        12:00:50 PM                                                   10:08:50 PM
                                                                  Wall Post ID 123456789                                            Private Message
ID 11111111                        Like Wall Post ID 00000000
                                         of User 123456
                                                                   Privacy: ALL_FRIENDS
                                                                         Type: Link
                                                                                                              6:24:50 PM
                                                                                                        Uploaded digital picture
                                                                                                                                      ID 00000001
                                                                                                                                   To Javier Rodrigez
UTC-2                                                                                                        ID 77777777
                                                                                                         Privacy: EVERYBODY
                                                                                                                                       ID 555555

            01:00 02:00 03:00 04:00 05:00 06:00 07:00 08:00 09:00 10:00 11:00 12:00 13:00 14:00 15:00 16:00 17:00 18:00 19:00 20:00 21:00 22:00 23:00

9/14/2011 12:00 AM                                                                                                                              9/15/2011 12:00 AM
                     Private Message
                       ID 00000000                               User Max Mustermann ID 4444444         User Javier Rodrigez ID 5555555
                       To John Doe                               Comment Wall Post ID 123456789        Comment Wall Post ID 123456789
                     ID 1111111111                                         2:08:50 PM                             3:44:50 PM
                        4:32:50 AM

              Figure 4: Anonymized Example Timeline for 24-hour period
to the social network operator) they introduce new challenges at the same
time as they solve others. One of the most obvious drawbacks is that the
data collection is hardly reproduceable: the timelines and graphs generated
will look differently for multiple runs as the social network is very dynamic
in nature, and the amount of data rather big. Within a single day a user
could change his social interconnection graph to large extend, and could try
to hide his or her communication in covert traffic. Some other data, like login
IP addresses as provided by the Facebook NeoPrint [3], are only available to
the operator of the network and not accessible with our method, neither with
an automated webbrowser or an API. Furthermore it is not easily possible
to guarantee completeness: once data is deleted by the user it can only be
undeleted from the social network operator, and is thus not available for our
analysis. We believe, however, that most of the data items are rather static
in nature, and that our methods are applicable and auxiliary for forensic
investigations.

5.3    Future Work

While some of the features are already implemented in our toolkit, we plan
on implementing the missing visualizations as well as the automated report
generation soon. We would also like to extend the number of supported social
networks. It would be very interesting to analyze how static social graphs
are, which is so far beyond the scope of this paper. This information could
be used to confirm the validity of our methods even further.
6    Conclusion

Social networks and the cloud computing paradigm will undoubtedly change
the way forensics examinations are done in the near future. In this paper we
identified valuable data sources in social networks, how they can be lever-
aged for analysis, and discussed to what extend this is possible without the
collaboration of the social network operator. Compared to traditional so-
cial analysis this can be done fully automated and allows cluster analysis as
well as different timeline visualizations. We implemented a proof-of-concept
application for creating social interconnection and social interaction graphs
(with whom is a user connected, with whom does the user interact often)
based on Facebook to show the feasibility of our approach, which we will
release as open source software.

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